In case of dropout [1]} \(\mathbf {z}_{l-1}\)  is a tensor of independent random variables i.i.d. of a Bernoulli distribution \(q(z)\) . Literature on filter pruning [2]} redefined \(\mathbf {z}_{l-1}\)  via (reparameterization trick [3]}) using a continuous differentiable function \(g\)  having learnable parameters \(\Phi \)  (also known as scaling factors) as:
\(\mathbf {h}_l = f(\mathbf {h}_{l-1}\odot g(\Phi _{l-1}^f,\epsilon ))\) 
